SOTAVerified

Quantization

Quantization is a promising technique to reduce the computation cost of neural network training, which can replace high-cost floating-point numbers (e.g., float32) with low-cost fixed-point numbers (e.g., int8/int16).

Source: Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers

Papers

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Showing 4376–4400 of 4925 papers

TitleStatusHype
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network AcceleratorsCode0
Quantization for OpenAI's Whisper Models: A Comparative AnalysisCode0
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit ViewCode0
Low-Precision Stochastic Gradient Langevin DynamicsCode0
Quantization-Free Autoregressive Action TransformerCode0
Exploring Post-Training Quantization of Protein Language ModelsCode0
Differentiable Product Quantization for End-to-End Embedding CompressionCode0
Quantization Guided JPEG Artifact CorrectionCode0
Wideband and Entropy-Aware Deep Soft Bit QuantizationCode0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-OffCode0
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTACode0
Neural Networks with Quantization ConstraintsCode0
Quantization in Spiking Neural NetworksCode0
Comprehensive Analysis of the Object Detection Pipeline on UAVsCode0
Low-Precision Random Fourier Features for Memory-Constrained Kernel ApproximationCode0
Low Precision Decentralized Distributed Training over IID and non-IID DataCode0
An Integrated Approach to Produce Robust Models with High EfficiencyCode0
Bees Local Phase Quantization Feature Selection for RGB-D Facial Expressions RecognitionCode0
Quantization NetworksCode0
NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealitiesCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
Accelerating Large-Scale Inference with Anisotropic Vector QuantizationCode0
BdSLW60: A Word-Level Bangla Sign Language DatasetCode0
BatchQuant: Quantized-for-all Architecture Search with Robust QuantizerCode0
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All datasetsImageNetCIFAR-10Wiki-40BAgeDB-30CFP-FPCOCO (Common Objects in Context)IJB-BIJB-CKnowledge-based:LFW

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FQ-ViT (ViT-L)Top-1 Accuracy (%)85.03—Unverified
2FQ-ViT (ViT-B)Top-1 Accuracy (%)83.31—Unverified
3FQ-ViT (Swin-B)Top-1 Accuracy (%)82.97—Unverified
4FQ-ViT (Swin-S)Top-1 Accuracy (%)82.71—Unverified
5FQ-ViT (DeiT-B)Top-1 Accuracy (%)81.2—Unverified
6FQ-ViT (Swin-T)Top-1 Accuracy (%)80.51—Unverified
7FQ-ViT (DeiT-S)Top-1 Accuracy (%)79.17—Unverified
8Xception W8A8Top-1 Accuracy (%)78.97—Unverified
9ADLIK-MO-ResNet50-W4A4Top-1 Accuracy (%)77.88—Unverified
10ADLIK-MO-ResNet50-W3A4Top-1 Accuracy (%)77.34—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_3MAP160,327.04—Unverified
2DTQMAP0.79—Unverified
#ModelMetricClaimedVerifiedStatus
1OutEffHop-Bert_basePerplexity6.3—Unverified
2OutEffHop-Bert_basePerplexity6.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy98.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy92.92—Unverified
#ModelMetricClaimedVerifiedStatus
1SSD ResNet50 V1 FPN 640x640MAP34.3—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-495.13—Unverified
#ModelMetricClaimedVerifiedStatus
1TAR @ FAR=1e-496.38—Unverified
#ModelMetricClaimedVerifiedStatus
13DCNN_VIVA_5All84,809,664—Unverified
#ModelMetricClaimedVerifiedStatus
1Accuracy99.8—Unverified